Tracking area optimization

ABSTRACT

In one embodiment, a system instantiated in memory is executed by a processor to: select cells to optimize in a mobile network where the cells are associated with at least one control signal load exceeding a threshold, where the threshold is for at least one of tracking area update (TAU) load or paging load, reconfigure the cell&#39;s tracking area (TA) by either adding at least one additional cell from another TA to the cell&#39;s TA and/or removing at least one other cell from the TA, temporarily implement the reconfigured TA in the mobile network, receive updated control signal data for cells affected by said reconfigured TA, and save the reconfigured TA in the mobile network if the at least one control signal load is under the threshold and the second control signal load is under an associated second control signal load threshold for the cells affected by the reconfigured TA.

TECHNICAL FIELD

The present invention generally relates to the optimizing of trackingareas in mobile networks.

BACKGROUND

The cells of a mobile network are typically organized into trackingareas (TAs). The mobile network uses TAs to determine an approximatelocation of a user equipment (UE) that is not in direct communicationwith the mobile network, e.g., when the UE is in an idle state.

There are at least two types of control signals used in conjunction withTAs in a mobile network. One such control signal is a tracking areaupdate (TAU) signal; when a UE is turned on or enters a cell associatedwith a new TA, it sends a TAU signal to the mobile network. The TAUsignal indicates to the mobile network that the sending UE is in one ofthe cells of a given TA. A second control signal, a paging signal, maybe sent by the mobile network to initiate communication with the UE, forexample, when facilitating an incoming call session. The paging signalis used to page the UE in each of the cells of the TA indicated by amost recently received TAU signal.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments of the disclosure will be understood and appreciatedmore fully from the following detailed description, taken in conjunctionwith the drawings in which:

FIGS. 1A-E are pictorial illustrations of mobile tracking areas inaccordance with embodiments described herein;

FIG. 2 is a schematic illustration of a tracking area optimizer serverconfigured and operative in accordance with embodiments described hereinto at least manage or optimize the tracking areas of FIGS. 1A-E; and

FIGS. 3-5 are flowcharts of optimization processes performed by theserver of FIG. 2 for the tracking areas of FIGS. 1A-E.

DESCRIPTION OF EXAMPLE EMBODIMENTS

Overview

A tracking area (TA) optimization system includes: a processor, amemory, and a TA optimization manager application instantiated in thememory and configured to be executed by the processor to at least: usecontrol signal data to select at least one cell to optimize in a TA of amobile network where the at least one cell is associated with at leastone control signal load that exceeds an associated control signal loadthreshold where the at least one control signal load is determinedaccording to the control signal data and is at least one of trackingarea update (TAU) load or paging load, reconfigure the TA to form areconfigured TA by at least one of: adding at least one additional cellfrom another TA to the TA or removing at least one other cell from theTA, temporarily implement the reconfigured TA in the mobile network, fora configurable period of time receive updated control signal dataassociated with both the at least one control signal load and a secondcontrol signal load for cells affected by the reconfigured TA, and upondetermining according to the updated control signal data that the atleast one control signal load is under the associated control signalload threshold and that the second control signal load is under anassociated second control signal load threshold for the cells affectedby the reconfigured TA, save the reconfigured TA for continued use inthe mobile network.

Detailed Description of Example Embodiments

It will be appreciated by one of skill in the art that the size andconfiguration of tracking areas (TAs) directly impact the amount ofPaging and Tracking Area Update signaling in a mobile network. A largerTA, i.e., a TA with relatively more cells, has a relatively greaterpaging load, and a relatively smaller TAU load. Conversely, reducing thesize of a given TA decreases the paging load, and increases the TAUload.

For example, in an extreme case of a mobile network where every cellbelongs to a single TA, once an initial TAU would be sent on power-on,UEs would not send any TAUs at all. However, every paging signal forevery UE would be sent to every cell in the mobile network. In theopposite case, where every cell in the network would be defined as aseparate TA, a UE would send a TAU every time it moved from one cell toanother, but paging signals would be sent to the UE only one cell at atime.

In accordance with embodiments described herein, a number of methods maybe used to optimize the size and configuration of TAs in order toconserve the network resources used for TAUs and paging signals.Thresholds may be set for the number of TAUs and/or paging signals to beassociated with a given cell. Cells that exceed one or more of thethresholds may be selected for optimization. The optimization mayleverage one or more key performance indicators (KPIs) to determine howan affected TA may be resized or reshaped in order to comply with thethresholds.

Reference is now made to FIG. 1A which is a pictorial illustration of anexemplary embodiment of a mobile network 10, constructed and operativein accordance with embodiments described herein. Mobile network 10 maybe implemented as any suitable mobile network known in the art, such as,for example, a third generation (3G) network, long term evolution (LTE)network, or a fifth generation (5G) network. Mobile network 10 isorganized into a multiplicity of TAs 20A-F (collectively referred toherein as “TAs 20”), where each one of TAs 20 (referred to herein as a“TA 20”) comprises a multiplicity of mobile cells. For example, TA 20Acomprises cells 31, 32, 33, 34, 35 and 36. It will be appreciated thateach cell may be defined as the coverage of a mobile base station, forexample, an eNodeB, physically located in the cell. As such, it will beappreciated that each cell in FIG. 1 may also represent a mobile basestation providing connectivity between UEs and mobile network 10.

It will be appreciated that a mobile network may typically have more orless TAs 20 than depicted in FIG. 1A, and the network's TAs 20 may havemore or less cells than cells 31-36 as depicted in FIG. 1A. It will alsobe appreciated that in the interests of clarity, the component cells ofTAs 20B-F may not be explicitly labelled herein.

In accordance with embodiments described herein, the size of one or moreof TAs 20 may be adjusted in order to optimize its TAU and paging loads.For example, per observation it may be determined by mobile network 10that the paging load in TA 20A is too high, i.e., that the number ofpaging signals in a given period of time exceeds the defined thresholdsfor the paging load. In response, as depicted in FIG. 1B, to whichreference is now made, TA 20A may be split into two TAs, hereinreferenced as TA20AA and TA 20AB. TA 20AA comprises cells 31, 32, and34; and TA 20AB comprises cells 33, 35, and 36. It will be appreciatedthat each of TA 20AA and AB comprises a subset of the cells in originalTA 20A, and accordingly, each will benefit from a relatively lowerpaging load.

It will also be appreciated, however, that the relatively lower pagingload may be accompanied by a higher TAU load, since additional TAUs maynow be sent for UEs being handed off between cells in TA 20AA and TA20AB, whereas previously such TAUs would not have been necessary. Forexample, in the exemplary embodiment of FIG. 1B, a UE moving from cell34 to cell 35 would send a TAU indicating a move from TA 20AA to TA20AB; whereas in the exemplary embodiment of FIG. 1A, such a move wouldnot represent a move into a new TA 20. Accordingly, depending on thecircumstances, it may not be desirable to split TA 20A into TA 20AA andTA 20AB, as it would lead to excess TAUs in the newly formed TAs.

Alternatively, instead of splitting TA 20A (FIG. 1A) into TA 20AA and TA20 AB, one or more component cells may be removed from TA 20A and addedto a different TA. For example, as depicted in FIG. 1C, to whichreference is now made, the paging load in original TA 20A may be reducedby moving cells 35 and 36 from original TA 20A to original TA 20D,thereby yielding TA 20A′ and TA 20D′ from the original TAs 20A and 20D.As will be described hereinbelow, cells 35 and 36 may be selected forremoval from TA 20A in accordance with a KPI for successful handovers.

It will be appreciated, that TA 20A′ may suffer an increase in TAU loadrelative to that experienced by original TA 20A; handovers between thecells in TA 20A′ and cells 35 and 36 in TA 20D′ may now be accompaniedby TAUs, whereas previously, when cells 35 and 36 were in TA 20A, suchhandovers would not necessitate TAUs. In some cases, the resultingincrease in TAU load may offset, or even exceed, the benefit realized bythe resulting decrease in paging load. Accordingly, in accordance withalternative embodiments described herein, instead of reducing the sizeof original TA 20A, mobile network 10 may instead reshape TA 20A togenerate TA 20A′ as depicted in FIG. 1D, to which reference is now made.

As depicted in FIG. 1D, mobile network 10 may reshape TA 20A by removingcells 35 and 36, as in the exemplary embodiment of FIG. 1C. However, inthe exemplary embodiment of FIG. 1D, cells 41 and 42 may also be addedto original TA 20A to generate TA 20A′. It will be appreciated that ifthe handover activity between original TA 20A and cells 41 and 42exceeds the handover activity between original TA 20A and cells 35 and36, the exemplary embodiment of FIG. 1D may represent an improvementvis-à-vis the exemplary embodiment of FIG. 1C with regard to TAU load.For example, cells 41 and 42 may include a highway that snakes in andout of some of the cells of original TA 20A (e.g., cells 32 and 43 inFIG. 1A). By including cells 41 and 42 in TA 20A′, the TAU load increasecaused by removing cells 35 and 36 may, in some cases, be more thanoffset by the TAU load decrease causing by adding cells 41 and 42??. Itwill be appreciated that the net effect of the exemplary embodiment ofFIG. 1D on the paging load for TA 20A (as depicted in FIG. 1A) vis-à-visTA 20A′ may be a function of how whether or not there are usually more,or less, UEs active in TA 20A or TA 20A′, and/or whether or not there isa difference in their usage patterns.

It will be appreciated that in accordance with some embodimentsdescribed herein, two or more TAs may be combined to form a single TA.For example, as depicted in FIG. 1E to which reference is now made, twoTAs (e.g., TAs 20C and 20F from FIG. 1A) with a high TAU load caused bya high rate of inter-TA hand overs may be combined to form TA 20CF. Itwill be appreciated that in the exemplary embodiment of FIG. 1E, thepaging load for TA 20CF may exceed that of the combined paging load fororiginal TAs 20C and 20F (as depicted in FIG. 1A). The benefit to bederived by generating TA 20CF may therefore be effectively limited bywhether or not original TAs 20C and 20F had a relatively low paging rateprior to their combination to form TA 20CF.

Reference is now made to FIG. 2 which is a schematic illustration of atracking area optimizer server 100, configured and operative inaccordance with embodiments described herein to at least manage theconfiguration and/or size of TAs as described with respect to FIGS.1A-E. Server 100 may be implemented using any suitable computingdevice(s) that may support the execution of TA optimization functions asdescribed herein. For example, server 100 may implemented usinghardware, software, and/or firmware on a multi-purpose personalcomputer, computer tablet, or smartphone. Server 100 may also beimplemented on a communications router or switch, or on a dedicatedInternet of Things (IoT) device. Server 100 may also be implemented asan integrated component on one or more of the elements of mobile network10, such as, for example, a mobility management entity (MME), a gateway(e.g., PGW, SGW), or a mobile base station (e.g., eNodeB).

Server 100 comprises processor 110, input/output (I/O) module 120, andmemory 130. Memory 130 may be implemented using any suitable storagemedium for storing software and/or operational data, such as an opticalstorage medium, a magnetic storage medium, an electronic storage medium,and/or a combination thereof. It will be appreciated that memory 130, orparts thereof, may be implemented as a physical component of server 100and/or as a physical component of one or more secondary devices incommunication with server 100. Memory 130 comprises at least TAoptimization manager 140 and self-optimizing network (SON) data 145.

Processor 110 may be operative to execute instructions stored in memory130. For example, processor 110 may be operative to execute TAoptimization manager 140 or a component module of TA optimizationmanager 140, e.g., TAU optimizer 150, paging optimizer 160, and/or TAshaper 170. It will be appreciated that server 100 may comprise morethan one processor 110. For example, one such processor 110 may be aspecial purpose processor operative to execute TA optimization manager140 or at least one of its component modules to optimize at least oneaspect of a TA's configuration. It will be appreciated that processor110 may be implemented as a central processing unit (CPU), and/or one ormore other integrated circuits such as application-specific integratedcircuits (ASICs), field programmable gate arrays (FPGAs), full-customintegrated circuits, etc. or a combination of such integrated circuits.

I/O module 120 may be any suitable software or hardware component suchas a universal serial bus (USB) port, disk reader, modem or transceiverthat may be operative to use protocols such as are known in the art tocommunicate either directly, or indirectly, with elements of mobilenetwork 10, such as, for example, a base station, an MME, a gateway,etc. over a communications network such as, for example, a backbonenetwork, the Internet, or via a WiFi connection. It will be appreciatedthat the embodiments described herein may also support configurationswhere some or all of the communications between I/O module 120 andelements of mobile network 10 are brokered by a local server andforwarded to I/O module 120 over the Internet, a local area network,and/or a suitable wireless technology. At least some of thefunctionality attributed herein to server 100 may be also performed onsuch a local server. It will similarly be appreciated that since I/Omodule 120 is operative to communicate with elements of mobile network10, the physical location of server 100 may not necessarily be withinclose proximity of any specific element(s) of mobile network 10.

TA optimization manager 140 and its component modules (e.g., TAUoptimizer 150, paging optimizer 160, and TA shaper 170) may be anapplication implemented in hardware or software that may be executed byprocessor 110 in order to at least configure TAs 20 based on SON data145.

Reference is now made to FIG. 3 which is flowchart of a TA pagingoptimization process 200 to be performed by TA optimization manager 140in accordance with embodiments described herein. For the purposes ofillustration, process 200 will be described herein respect to theexemplary mobile network depicted in FIGS. 1A-E, i.e., mobile network10.

TA optimization manager 140 may define (step 205) thresholds for thenumber of TAUs and paging signals in the cells of mobile network 10 overa defined period of time. For example, the TAU threshold for cell 35 maybe defined as 2,000 TAUs in an hour, and the paging signal threshold forcell 35 may be defined as 2,500 paging signals in hour. It will beappreciated that the relative values for the TAU and paging signalthresholds presented herein are by way of example only. In practice, theTAU threshold may alternatively be set at the same value as the pagingsignal threshold; similarly, the paging signal threshold may be set at alower value than that of the TAU threshold.

The thresholds may be input manually into TA optimization manager 140via a suitable interface, such as, for example, a mouse, keyboard, or avoice activated command interface. Alternatively, the thresholds may beuploaded from an external source for use by TA optimization manager 140via I/O module 120. Alternatively, as will be described hereinbelow, TAoptimization manager 140 may be configured to calculate such thresholdsduring a setup phase when the TAs are initially defined for the cells ofa mobile network. It will be appreciated that the TAU and pagingthresholds may be defined on a per cell basis. Alternatively, some orall of the thresholds may be defined on an individual TA basis, wherethe thresholds for all of the cells in a given TA may be definedidentically. For example, cells 31-35 in TA 20A may all be defined withthe same thresholds. Alternatively, some or all of the thresholds may bedefined on a group basis, where, for example, values may be assigned forclusters of TAs, or according to tracking area lists (TALs), which areused by UEs to identify entry to a new TA, thereby prompting a TAU.

TA optimization manager 140 may receive (step 210) paging data for theTAs in mobile network 10. In accordance with embodiments describedherein, such paging data may be accumulated with other operational datafrom the cells in mobile network 10 and accumulated and stored forreference by TA optimization manager 140 as SON data 145. Accordingly,step 210 may also involve TA optimization manager 140 receiving therelevant paging data from SON data 145.

TA optimization manager 140 may invoke paging optimizer 160 to identify(step 215) cells where the paging load as determined according to thepaging data received from SON data 145 exceeds their respective definedpaging thresholds. Paging optimizer 160 may select (step 220) anidentified cell to optimize by adjusting its associated TA. For example,per the exemplary embodiment of FIG. 1A, cell 33 in TA 20A may exceedits defined paging threshold, and paging optimizer 160 may select it foroptimization. The selection of specific cells in step 220, may be afunction of how much the actual paging signals exceed the definedthreshold, either in absolute, or relative, terms.

Paging optimizer 160 may identify (step 225) one or more neighboringcells to remove from the associated TA (e.g., TA 20A) according to oneor more KPIs that indicate that there may be relatively low trafficbetween the selected cell and the identified neighboring cells. Forexample, paging optimizer 160 may use a KPI for handover (HO) successrate; a relatively low handover success rate between the selected celland one or more of its neighboring cells may indicate that a UE in theselected cell is relatively unlikely to migrate to one of the identifiedneighboring cells. Alternatively, or in addition, may use a KPIassociated a “ping pong” rate for successful hand overs, which may beindicative of the rate at which a UE crosses back and forth over theboundary of two cells. It will be appreciated that the data for this KPImay be retrieved from SON data 145 by paging optimizer 160.

Using the exemplary embodiment of FIG. 1A to illustrate, if the selectedcell is cell 33 and neighboring cells 35 and 36 have a relatively lowhandover success rate with cell 33, it may indicate that a UE known tobe in cell 33 may be unlikely to migrate to either of cells 35 or 36.Accordingly, when attempting to locate the UE, it may not be necessaryfor mobile network 10 to include cell 33 with cells 35 and 36 in thesame paging area, i.e., the same TA. Paging optimizer 160 may thereforeremove (step 230) the one or more identified neighboring cells (e.g.,cells 35 and 36) from the associated TA. Step 230 may therefore yieldthe exemplary embodiment depicted in FIG. 1C, where new TAs 20A′ and20D′ may be formed by transferring cells 35 and 36 from original TA 20Ato original TA 20D.

It will be appreciated that the number of cells to remove in step 230may be configurable and/or a function of the number of cells identifiedin step 225. Accordingly, as depicted in FIG. 3, step 230 is depicted interms of “N” neighbors to represent the number of neighboring cells toremove. As shown in the exemplary embodiment of FIG. 1C, “N” may beequal to two. However, the embodiments described herein may supportother values for “N”.

TA optimization manager 140 may receive (step 235) updated paging andTAU data via I/O module 120 from the devices in mobile network 10. Theupdated paging and TAU data is based on the performance of the cellselected in step 220, as well as any other cells presumably affected bystep 230, for example, the other cells in TA 20A′ and TA 20D′. Theupdated paging and TAU data is gathered for a configurable period oftime, such as, for example, an hour, two hours, four hours, a day, etc.

Using the updated paging and TAU data, TA optimization manager 140 maydetermine whether the paging and TAU loads are now under the thresholdsfor the optimized cell as well as the affected cells. It will beappreciated that in some instances, the performance of step 230 may not,over time, necessarily succeed in lowering the paging load under thepaging load threshold in the optimized cell. Furthermore, while theperformance of step 230 may indeed lower the paging load under thepaging load threshold in the optimized cell, the reduction in size ofthe original TA may result in an increased TAU load, such that the TAUload of the optimized cell may now exceed the TAU load threshold. Also,even if both the paging and TAU loads are under their respectivethresholds in the optimized cell, it is possible that the performance ofstep 230 negatively impacted some of the other affected cells (e.g., thecells of TAs 20′ and 20D′ per FIG. 1C), such that their paging and/orTAU loads now exceed their respective defined thresholds.

If the paging and/or TAU loads for the optimized cell and/or any of theaffected cells increases subsequent to the performance of step 230 suchthat at least one of the loads exceeds its associated threshold, TAoptimization manager 140 may back out (step 241), i.e., reverse, thechanges performed in step 230. It will be appreciated however, that inthe event that the paging and/or TAU load in an affected cell hadalready exceeded its associated threshold prior to the performance ofstep 230, the performance of step 241 may be contingent on whether ornot the situation for the affected cell has worsened vis-à-vis theprevious situation, i.e., whether or not the amount by which the pagingor TAU load exceeds its respective threshold has increased.

If per the updated paging and TAU data (as received in step 235) thepaging and TAU loads for the affected cells are less than theirrespective defined thresholds, TA optimization manager 140 may save(step 245) the new TA configuration. For example, per the exemplaryembodiment of FIG. 1C, mobile network 10 may be permanently updated withTAs 20A′ and 20D′ in place of original TAs 20A and 20D.

It will be appreciated that the exemplary embodiment of FIG. 1Crepresents one of many possible outcomes from the operation of process200. For example, in accordance with an exemplary embodiment, one ormore of the cells may already have a paging load close to, or in excessof, its associated paging threshold. In such a case, it may beforeseeable that moving cells 35 and 36 to TA 20D may not be successfuland that such a move would likely necessitate the performance of theback out of changes in step 241. For example, this may be foreseeablebased on a previous, unsuccessful attempt to move cells 35 and 36 to TA20D, or may alternatively be determined by paging optimizer 160 in viewof the paging load statistics reviewed in step 225.

In accordance with embodiments described herein, if step 225 isunsuccessful in that paging optimizer 160 cannot identify cells to moveto another TA 20, paging optimizer may divide a TA 20 into two (or more)TAs 20 in step 230. For example, as depicted in the exemplary embodimentof FIG. 1B, paging optimizer 160 may divide the cells of TA 20A into TAs20 AA and TA 20AB.

As noted hereinabove, the optimization of a given cell may affect notonly the other cells in the same TA, but also the cells in a neighboringTA. For example, the results of the optimization of cell 33 in FIG. 1Amay affect not only the other cells in TA 20A, and also the cells in TA20D. As depicted in FIG. 1C, TA 20D′ may be larger than original TA 20D;thus illustrating that the cells in a neighboring TA may also beaffected by the optimization of a cell in another TA. In accordance withembodiments described herein, such affected cells may be excluded fromdirect optimization for a period of time after they are affected by theoptimization of another cell. In such manner, the determination of step240 may be performed on the basis of the performance of step 230,without interference from other optimization measures.

Accordingly paging optimizer 160 may determine whether there are anyremaining non-affected cells to optimize in mobile network 10 (step250), i.e., that have not been directly optimized or otherwise affectedin a recent iteration of process 200. It will be appreciated that thatcells that have been optimized and/or affected by process 200 may beexcluded in such manner for a configurable period of time, e.g., two,hours, four hours, twelve hours, a day, etc.

If per the determination in step 250 there are remaining cells tooptimize in mobile network 10, process control may return to step 220,where another cell may be selected from as yet unaffected cells fromamong the population of cells that was identified in step 215.Otherwise, process control may return to step 205 and after aconfigurable period of time, process 200 may start over. Alternatively,step 205 may not be performed in each iteration of process 200, and someiterations of process 200 may begin with step 210.

Reference is now made to FIG. 4 which is a flowchart of another TAUoptimization process 300 performed by TA optimization manager 140 tofurther optimize the configuration of TAs, such as, for example, TAs 20as depicted in (FIGS. 1A-E). In accordance with embodiments describedherein, process 300 may optimize TAs 20 by leveraging excess TAU loadcapacity. TA optimization manager 140 may employ TAU optimizer 150 toperform some, or all, of the functionality of process 300.

In accordance with embodiments described herein, process 300 may beperformed subsequent to process 200 in order to further optimize TAs 20in mobile network 10 by addressing cells with TAU loads above theirthresholds that were not directly or indirectly affected during a recentiteration of process 200. For example, process 300 may be employed tooptimize cells for which changes implemented during process 200 werebacked out in step 241. Process 300 may also be employed to optimizecells with high TAU loads that were not affected by a recent iterationof process 200 due to low paging loads. In accordance with otherembodiments described herein, process 300 may be performed prior toprocess 200.

TA optimization manager 140 may define (step 305) thresholds for thenumber of TAUs and paging signals in the cells of mobile network 10 overa defined period of time. TA optimization manager 140 may perform step305 in generally the same manner as step 205 in process 200. It will beappreciated that when process 300 is performed within a given period oftime after process 200, it may not be necessary to perform step 305, asthe relevant thresholds have already been defined, such that the TAU andpaging thresholds may have already been defined for the cells in network10.

TA optimization manager 140 may receive (step 310) TAU data for the TAsin mobile network 10. Similar to step 210 in process 200, such TAU datamay be accumulated with other operational data from the cells in mobilenetwork 10 and accumulated and stored for reference by TA optimizationmanager 140 as SON data 145. Accordingly, step 310 may involve TAoptimization manager 140 receiving the relevant TAU data from SON data145.

TA optimization manager 140 may invoke TAU optimizer 150 to identify(step 315) a population of cells where the TAU load, as determinedaccording to the TAU data received from SON data 145, exceeds theirrespective defined TAU thresholds. TAU optimizer 160 may select (step320) a cell to optimize by attempting to reduce its associated TAU load.For example, per the exemplary embodiment of FIG. 1C, cell 32 in TA 20A′may exceed its defined TAU threshold, and TAU optimizer 150 may selectit for optimization. The selection of specific cells in step 220, maybe, for example, a function of how much the actual TAU load exceeds thedefined threshold, either in absolute, or relative, terms.

It will be appreciated that in step 320, TAU optimizer 160 may notselect cells that have recently been affected, either directly orindirectly, by another optimization process, e.g., process 200.Accordingly, if TA 20A was recently configured by process 200 (asdescribed hereinabove) to generate TA 20A′, cell 32 may be considered tohave been affected by a recent optimization process, and would thereforenot be available for selection in a current iteration of step 320. Itwill be appreciated that a configurable period of time may be employedby TAU optimizer 160 to determine whether or not a cell has beenrecently affected in such manner.

TAU optimizer 150 may identify (step 325) one or more neighboring cellsto add to the associated TA (e.g., TA 20A′) according to one or moreKPIs that indicate that there may be a relatively high rate of trafficbetween the selected cell and the identified neighboring cells. Forexample, TAU optimizer 150 may use the KPI for handover success ratediscussed hereinabove; a relatively high handover success rate betweenthe selected cell and one or more of its neighboring cells may indicatethat a UE in the selected cell is relatively likely to migrate to one ofthe identified neighboring cells. It will be appreciated that TAUoptimizer 150 may retrieve the data for the KPI from SON data 145.

For example, referring to the embodiment of FIG. 1C, the cell beingoptimized may be cell 32 of TA 20A′. In step 325, TAU optimizer 150 mayidentify cells 41 and 42 of TA 20B as having high handover success ratesfor handovers to and from cell 32. The high handover success rate may bedetermined as a function of a handover success rate threshold. It willbe appreciated that handovers between cell 32 and cells 41 and 42 maycontribute to the high TAU load observed for cell 32. Accordingly, TAUoptimizer 150 may add (step 330) the one or more identified neighboringcells (e.g., cells 35 and 36) to the associated TA, e.g., TA 20A′ perthe embodiment of FIG. 1C.

It will be appreciated that the number of cells to add in step 330 maybe configurable and/or a function of the number of cells identified instep 325. Accordingly, as depicted in FIG. 4, step 330 is depicted interms of “J” neighbors to represent the number of neighboring cells toadd. As shown in the exemplary embodiment of FIG. 1C, “J” may be equalto two. However, the embodiments described herein may support othervalues for “J”.

Step 330 may therefore yield the exemplary embodiment depicted in FIG.1D, where new TAs 20A″ and 20B′ may be formed by transferring cells 41and 42 from original TA 20B to original TA 20A′.

TA optimization manager 140 may receive (step 335) updated paging andTAU data from via I/O module 120 from the devices in mobile network 10.The updated paging and TAU data is based on the performance of the cellselected in step 320, as well as any other cells presumably affected bystep 330, for example, the other cells in TA 20A″ and TA 20B′. Theupdated paging and TAU data is gathered for a configurable period oftime, such as, for example, an hour, two hours, four hours, a day, etc.

Using the updated paging and TAU data, TA optimization manager 140 maydetermine whether the paging and TAU loads are now under the thresholdsfor the affected cells (step 340). It will be appreciated that in someinstances, the performance of step 330 may not necessarily succeed inlowering the TAU load under the TAU load threshold in the optimizedcell, i.e., cell 32 in FIG. 1D. Furthermore, while the performance ofstep 330 may indeed lower the TAU load under the TAU load threshold inthe optimized cell, the increase in size of the original TA may resultin an increased paging load, such that the paging load of the optimizedcell may now exceed the paging load threshold. Also, even if both thepaging and TAU loads are under their respective thresholds in theoptimized cell, it is possible that the performance of step 330 impactedon some of the other affected cells (e.g., the cells of TAs 20A′ and 20Bper FIG. 1C), such that their paging and/or TAU loads now exceed theirrespective defined thresholds.

If the paging and/or TAU loads for the optimized cell and/or any of theaffected cells increases subsequent to the performance of step 330 suchthat at least one of the loads exceeds its associated threshold, TAoptimization manager 140 may back out (step 341), i.e., reverse, thechanges performed in step 330. It will be appreciated however, that inthe event that the paging and/or TAU load in an affected cell hadalready exceeded its associated threshold prior to the performance ofstep 330, the performance of step 341 may be contingent on whether ornot the situation for the affected cell has worsened vis-à-vis theprevious situation, i.e., has the amount by which the paging or TAU loadexceeds its respective threshold increased.

If per the updated paging and TAU data (as received in step 335), thepaging and TAU loads for the affected cells are less than theirrespective defined thresholds, TA optimization manager 140 may save(step 345) the new TA configuration. For example, per the exemplaryembodiment of FIG. 1D, mobile network 10 may be permanently updated withTAs 20A″ and 20B′ in place of original TAs 20A′ and 20B.

TAU optimizer 150 may determine whether there are any remainingnon-affected cells to optimize in mobile network 10 (step 350), i.e.,that have not been directly optimized or otherwise affected in thecurrent iteration of process 300 and/or a recent iteration of eitherprocess 200, or process 300. It will be appreciated that cells that haveoptimized and/or affected by either process 200 or process 300 may beexcluded in such a manner for a configurable period of time, e.g., two,hours, four hours, twelve hours, a day, etc.

If per the determination in step 350 there are remaining cells tooptimize in mobile network 10, process control may return to step 320,where another cell may be selected from as yet unaffected cells fromamong the population of cells that was identified in step 315.Otherwise, process control may return to step 305 and after aconfigurable period of time, process 300 may start over. Alternatively,step 305 may not be performed in each iteration of process 300, and someiterations of process 300 may begin with step 310.

It will be appreciated that process 300 may also be used to combine twoor more TAs as per the exemplary embodiment of FIG. 1E. For example, oneof the cells in original TA 20C (FIGS. 1A-D) may have been selected foroptimization in step 320. In step 325, several, or perhaps all, of thecells in original TA 20D (FIGS. 1A-D) may have been identified with highhand over success rates. In step 330 may adjust the value of “J” torepresent all of the cells of TA 20D. In accordance with someembodiments described herein, TAU optimizer 150 may factor in a ratiofor current TAU and paging signal loads for the affected cells whendetermining a value for “J”. For example, if the affected cells of TAs20C and 20D have relatively low paging signal loads accompanied byrelatively high TAU loads, it may be possible to reduce the TAU loadswithout exceeding the paging load thresholds by combining TAs 20C and20D. TAU optimizer 150 may therefore set “J” to a value that wouldinclude all of the cells in TA 20D, thereby rendering the result asdepicted in FIG. 1E.

It will be appreciated that processes 200 and 300 as describedhereinabove may be employed by TA optimization manager 140 to optimizepaging and TAU loads by adjusting the size of affected TAs 20. It willbe appreciated, however, that as described hereinabove, in some casesprocesses 200 and 300 may not successfully optimize a TA; after addingand/or removing cells to/from the TA, the paging load and/or the TAUload may still exceed defined threshold(s). In some cases, a TA may havean optimal size, yet the composition of the TA, i.e., the allocation ofthe cells in the TA, may be sub-optimal.

In accordance with embodiments described herein, UE traffic patterns maybe leveraged to reshape a TA to optimize paging and TAU loads. Forexample, if a TA 20 is reshaped to include cells that approximate atleast a majority of a typical UE's trajectory as it moves through the TA20, the TAU load may be decreased since the UE may enter/exit the TA 20less often. Similarly, the paging load may be decreased since thelikelihood would increase that the UE would be in a given cell receivingthe page; “unnecessary” paging signals would be offloaded to another TA20.

Reference is now made to FIG. 5 which is a flowchart of a TA pagingoptimization process 400 to be performed by TA optimization manager 140in accordance with embodiments described herein. Process 400 may beperformed on a periodic basis before, or after, the performance ofprocess 200 and/or process 300. Alternatively, or in addition, process400 may be performed on a per cell basis by TA optimization manager 140on cells that were previously processed unsuccessfully by process 200and/or process 300, i.e., where attempts to optimize the performance ofa cell by adding and/or removing cells from an associated TA wereunsuccessful.

TA optimization manager 140 may define (step 405) thresholds for thenumber of TAUs and paging signals in the cells of mobile network 10 overa defined period of time. TA optimization manager 140 may perform step405 in generally the same manner as steps 205/305 in process 200/300. Itwill therefore be appreciated that when process 400 is performed withina given period of time after process 200/300, it may not be necessary toperform step 405, as the relevant thresholds have already been defined,such that the TAU and paging thresholds may have already been definedfor the cells in network 10.

TA optimization manager 140 may receive (step 310) TAU and paging datafor the TAs in mobile network 10. Functionally similar to theperformance of both step 210 in process 200 and step 310 in process 300,such TAU and paging data may be accumulated with other operational datafrom the cells in mobile network 10 and accumulated and stored forreference by TA optimization manager 140 as SON data 145. Accordingly,step 410 may involve TA optimization manager 140 receiving the relevantTAU data from SON data 145.

TA optimization manager 140 may invoke TA shaper 170 to identify (step415) a population of cells where at least one of the TAU load or thepaging load (as determined according to the data received from SON data145) exceeds a respective defined threshold. TA shaper 170 may select(step 420) a cell to optimize by reshaping its configuration. Forexample, per the exemplary embodiment of FIG. 1A, the paging load and/orTAU load in cell 32 in TA 20A may exceed its associated threshold, andTA shaper 170 may select it for optimization. The selection of specificcells in step 420, may be, for example, a function of how much theactual TAU/paging load exceeds the defined threshold, either inabsolute, or relative, terms.

It will be appreciated that in step 420, TA shaper 170 may not selectcells that have recently been affected, either directly or indirectly,by another optimization process, e.g., process 200 or process 300.Accordingly, if TA 20A was recently configured by process 200 or process300 (step 422), cell 32 may be considered to have been affected by arecent optimization process, and would therefore not be available foroptimization in a current iteration of process 400. It will beappreciated that a configurable period of time may be employed by TAshaper 170 to determine whether or not a cell has been recently affectedin such manner.

It will also be appreciated that, as noted hereinabove, TA optimizationmanager 140 may perform process 400 in order to optimize a specific cellor TA 20 for which the optimization of processes 200 and/or 300 wereunsuccessful, i.e., that changes to the associated TA were backed out ineither step 241 of process 200, or step 341 of process 300. In such acase, steps 405-422 may be unnecessary and process 400 may be startedfrom step 425.

TA shaper 170 may determine (step 425) a popular trajectory for UEsmoving through the cell selected for optimization. For example, usingthe exemplary embodiment of FIG. 1A, cell 32 may be selected foroptimization. TA shaper 170 may use inter-cell handover success rate totrack typical traffic flows for UEs as they move into and out of cell32. For example, the most popular trajectory of UEs reaching cell 32 mayconform to the route of a highway that runs through cells 33 and 34 ofTA 20A, crosses over to a cell in TA 20B, returns to TA 20A in cell 32,and then continues to a different cell in TA 20B.

TA shaper 170 may shape (step 430) TA 20A based on the most populartrajectory involving the cell selected for optimization, e.g., cell 32.It will be appreciated that UEs in cars moving along the highway maygenerate a relatively high TAU load for the cells of TA 20A as they movein and out of TA 20. It will also be appreciated that, as per theexample, the trajectories of UEs in cells 35 and 36 may be less likelyto include cell 32; cells 35 and 36 may be relatively infrequentlyvisited by UEs migrating to and from cell 32. Accordingly, the TAU loadfor cell 32 may be reduced by adding the neighboring cells from TA 20B,and the paging load may be reduced by removing cells 35 and 36 from TA20A. The exemplary embodiment of FIG. 1D may depict the result of step430: cells 35 and 36 have been added to TA 20D to form TA 20D′; andcells 41 and 42 have been added to TA 20A to form TA 20A″ and yield TA20B′.

It will be appreciated that the number of cells added and removed instep 430 may be configurable and/or a function of the number of cells inthe popular trajectory identified in step 425. Similarly, in someembodiments the number of cells removed may not necessarily equal thenumber of cells added.

TA optimization manager 140 may receive (step 435) updated paging andTAU data via I/O module 120 from the devices in mobile network 10. Theupdated paging and TAU data is based on the performance of the cellselected in step 420, as well as any other cells presumably affected bystep 330, for example, the other cells in TA 20A″, TA 20B′, and TA 20D′in FIG. 1D. The updated paging and TAU data is gathered for aconfigurable period of time, such as, for example, an hour, two hours,four hours, a day, etc.

TA optimization manager 140 may use the updated paging and TAU data todetermine whether the paging and TAU loads are now under the thresholdsfor the affected cells (step 440). It will be appreciated that in someinstances, the performance of step 430 may not necessarily succeed inlowering the TAU and/or paging loads under their respective thresholdsin the optimized cell, i.e., cell 32 in FIG. 1D. Furthermore, while theperformance of step 430 may indeed optimize the TAU and paging loads, itis possible that the performance of step 430 impacted some of the otheraffected cells (e.g., the cells of TA 20A″, TA 20B′, and TA 20D′ in FIG.1D), such that their paging and/or TAU loads now exceed their respectivedefined thresholds.

If the paging and/or TAU loads for the optimized cell and/or any of theaffected cells increases subsequent to the performance of step 430 suchthat at least one of the loads exceeds its associated threshold, TAoptimization manager 140 may back out (step 441), i.e., reverse, thechanges performed in step 430. It will be appreciated however, that inthe event that the paging and/or TAU load in an affected cell hadalready exceeded its associated threshold prior to the performance ofstep 330, the performance of step 441 may be contingent on whether ornot the situation for the affected cell has worsened vis-à-vis theprevious situation, i.e., if the amount by which the paging or TAU loadexceeds its respective threshold has increased.

If per the updated paging and TAU data (as received in step 435) thepaging and TAU loads for the affected cells are less than theirrespective defined thresholds, TA optimization manager 140 may save(step 445) the new TA configuration. For example, per the exemplaryembodiment of FIG. 1D, mobile network 10 may be permanently updated withTAs 20A″, 20B′ in place of original TAs 20A′ and 20B.

TAU optimizer 150 may determine whether there are any remainingnon-affected cells to optimize in mobile network 10 (step 450), i.e.,that have not been directly optimized or thereby affected in the currentiteration of process 400 and/or a recent iteration of either process200, or process 300. It will be appreciated that cells that have beenoptimized and/or affected by either process 200 or process 300 may beexcluded in such manner for a configurable period of time, e.g., two,hours, four hours, twelve hours, a day, etc.

If per the determination in step 450 there are remaining cells tooptimize in mobile network 10, process control may return to step 420,where another cell may be selected from as yet unaffected cells fromamong the population of cells that was identified in step 415.Otherwise, process control may return to step 405 and after aconfigurable period of time, process 400 may start over. Alternatively,step 405 may not be performed in each iteration of process 400, and someiterations of process 400 may begin with step 410.

In accordance with some embodiments described herein, TA shaper 170 mayalso be operative to address “ping-pong” TAU activity where UEsrepeatedly crosses back and forth over the boundary of two TAs 20. Itwill be appreciated that such activity may generate an increase in TAUactivity for all of the cells in both of their respective TAs 20. Inorder to account for ping-pong activity, step 425 may also comprisedetecting a ping-pong trajectory. For example, as per the exemplaryembodiment of FIG. 1C, there may be a high rate of ping-pong activitybetween cell 32 in TA 20A′ and cell 41 in TA 20B. TA shaper 170 may useavailable KPIs for the number of times a UE Ping Pongs between two cellsto map the ping-pong trajectory between cell 32 and cell 41. If thefrequency of ping-pong activity exceeds a defined threshold, in step 430TA shaper 170 may shape the affected TAs 20 to ensure that cells 32 andcell 41 are in the same TA 20. For example, if cell 32 was the cellselected for optimization in step 420, cell 41 may be added to TA 20A′;if cell 41 was the cell selected for optimization in step 420, cell 32may be added to TA 20B.

In accordance with embodiments described herein, TA optimization manager140 may also be operative to reconfigure TAs 20 to address sudden spikesin TAU loads that may occur when large numbers of UEs enter/exit a TA atthe same time. For example, at mass transport terminals such as airportsor train/bus stations, a large number of people may cross a TA boundaryat generally the same time, resulting in a sudden, short surge of TAUsignaling.

TA optimization manager 140 may attempt to prevent such sudden spikes byusing multiple versions of the TAs 20 in the vicinity of a locationwhere the spikes are observed to occur regularly. For example, using theexemplary embodiment of FIG. 1D, a train station may be located in cell42. TA optimization manager 140 may detect a spike in TAUs from cell 42when a train arrives in the station and several UEs on board the trainsend TAUs at roughly the same time. In response, TA optimization manager140 may effectively stagger a next wave of TAUs by instructing the MMEto provide different TAL versions to the UEs in cell 42. Some of the UEsmay receive a TAL referencing TA 20A″ as shown in FIG. 1D. Other UEs mayreceive a TAL referencing TA 20B as shown in FIG. 1A. Other UEs mayreceive a TAL for a third TA 20 that may be formed, for example, byadding cell 42 to TA 20D as shown in FIG. 1A. It will be appreciatedthat by assigning staggered TALs to different UEs, a next spike in TAUsmay be prevented when the UEs cross another TA boundary.

Alternatively, or in addition, TA optimization manager 140 may useanalysis of SON data 145 to anticipate such sudden spikes before theyoccur, based on historical TAU trends. It will be appreciated that TAoptimization manager 140 may be configured to use machine learningalgorithms such as are known in the art to detect regular patterns toanticipate upcoming TAU spikes. In such a case, TA optimization manager140 may instruct the MME to provide staggered TALs to UEs in theaffected cell (e.g., cell 42) regardless of whether or not a spike inTAU signals was actually observed in real time.

It will be appreciated that processes 200, 300, and/or 400 may beconfigured to run on a periodic basis with intervening “sleep” periodsduring which additional SON data 145 may be collected. For example, 200,300, and/or 400 may be configured to run at daily, weekly, monthly, etc.intervals using SON data 145 from a time period generally correspondingto an associated processing interval.

In accordance with embodiments described herein, TA optimization manager140 may also be operative to provide an initial configuration oftracking areas 20 during deployment of mobile network 10. TAoptimization manager 140 may employ known metrics such as thegeographical locations of mobile base stations, azimuth and beam-widthsettings, and cell coverage range as indicated by timing advance and/orpropagation delay information as inputs to a tracking area initialconfiguration process.

In accordance with the tracking area initial configuration process,co-sector cells, co-located cells and/or cells within a configureddistance (e.g. 1 km) will initially be considered to be part of a sametracking area. To determine a TAL, cells with location (latitude andlongitude) information may be divided into a configured number ofdistinct groups using a graph theory algorithm for partitioningconnected weighted graphs using distance as the weight. The resultingdistinct graphs may be allocated to unique tracking area lists, based ontracking areas present in a distinct group.

It will be appreciated that the embodiments described herein may provideoptimization of the size and/or shape of tracking areas in order to moreoptimally manage paging and tracking area updates in a mobile network.It will similarly be appreciated that the embodiments described hereinmay leverage KPIs for geographic information and/or KPIs readilyderivable from existing network elements to optimize the tracking areas.Accordingly, some, or all of the disclosed system and/or methods may beimplemented in software without specialized hardware and/or firmwareupgrades. Furthermore, each change to a tracking area configuration maybe reviewed after a period of time to assess the efficacy of the change,both in terms of the focus of the change, i.e., a given cell and/or itsassociated tracking area, as well as other cells that may be affected bythe change.

It will also be appreciated that the embodiments described hereinaddress both paging and tracking area update loads in order to providean optimal overall configuration. It will similarly be appreciated thatthe embodiments described herein may focus on addressing cells withsalient load issues in order to more equitably redistribute paging/TAUloads throughout a given tracking area and/or network segment.

It will also be appreciated that the embodiments described herein maysupport periodic autonomous operation, thereby providing enhancednetwork performance without necessitating manual operation.

It is appreciated that software components of the embodiments of thedisclosure may, if desired, be implemented in ROM (read only memory)form. The software components may, generally, be implemented inhardware, if desired, using conventional techniques. It is furtherappreciated that the software components may be instantiated, forexample: as a computer program product or on a tangible medium. In somecases, it may be possible to instantiate the software components as asignal interpretable by an appropriate computer, although such aninstantiation may be excluded in certain embodiments of the disclosure.

It is appreciated that various features of the embodiments of thedisclosure which are, for clarity, described in the contexts of separateembodiments may also be provided in combination in a single embodiment.Conversely, various features of the embodiments of the disclosure whichare, for brevity, described in the context of a single embodiment mayalso be provided separately or in any suitable subcombination.

It will be appreciated by persons skilled in the art that theembodiments of the disclosure are not limited by what has beenparticularly shown and described hereinabove. Rather the scope of theembodiments of the disclosure is defined by the appended claims andequivalents thereof:

What is claimed is:
 1. A tracking area (TA) optimization apparatuscomprising: a processor; a memory; and a TA optimization managerapplication, instantiated in said memory and configured to be executedby said processor to at least: use control signal data to select atleast one cell to optimize in a TA of a mobile network, wherein said atleast one cell is associated with at least one control signal load thatexceeds an associated control signal load threshold, wherein said atleast one control signal load is determined according to said controlsignal data, and is at least one of tracking area update (TAU) load orpaging load, reconfigure said TA to form a reconfigured TA by at leastone of: adding at least one additional cell from another TA to said TA,or removing at least one other cell from said TA, temporarily implementsaid reconfigured TA in said mobile network, for a configurable periodof time, receive updated control signal data associated with both saidat least one control signal load and a second control signal load forcells affected by said reconfigured TA, and upon determining accordingto said updated control signal data that said at least one controlsignal load is under said associated control signal load threshold andthat said second control signal load is under an associated secondcontrol signal load threshold for said cells affected by saidreconfigured TA, save said reconfigured TA for continued use in saidmobile network.
 2. The TA optimization apparatus according to claim 1wherein said cells affected by said reconfigured TA include at least allother cells in said reconfigured TA.
 3. The TA optimization apparatusaccording to claim 1 wherein said cells affected by said reconfigured TAinclude at least one cell in at least one neighboring TA, wherein saidat least one neighboring TA was reconfigured by said TA optimizationapparatus performing at least one of: removing said one additional cellfrom said at least one neighboring TA to be added to said TA, or addingsaid at least one other cell removed from said TA to said at least oneneighboring TA.
 4. The TA optimization apparatus according to claim 1wherein said TA optimization manager is further configured to wait aconfigurable period of time after said reconfigured TA is saved forcontinued use in said mobile network before selecting said at least onecell to be optimized from among said cells affected by said reconfiguredTA.
 5. The TA optimization apparatus according to claim 1 wherein saidTA optimization manager is further configured to select said at leastone cell according to a measure of how much said at least one controlsignal load exceeds said associated control signal load threshold,wherein said measure is either a percentage or an absolute amount. 6.The TA optimization apparatus according to claim 1 wherein said TAoptimization manager is further configured to use at least inter-cellhandover success rate to determine which at least one additional cellfrom another TA to add to said TA, and which at least one other cell toremove from said TA.
 7. The TA optimization apparatus according to claim1 wherein said TA optimization manager is further configured to use: afirst number of additional cells to determine a number of cells to beadded when adding said at least one additional cell from another TA tosaid TA; and a second number to determine a number of other cells to beremoved when removing said at least one other cell from said TA.
 8. TheTA optimization apparatus according to claim 7 wherein at least one ofsaid first number and second number is configurable.
 9. The TAoptimization apparatus according to claim 7 wherein said first number isdetermined according to inter-cell handover success rates between saidat least one cell and said at least one additional cell from another TA.10. The TA optimization apparatus according to claim 7 wherein saidsecond number is determined according to inter-cell handover successrates between said at least one cell and said at least one other cellfrom said TA.
 11. The TA optimization apparatus according to claim 1wherein said TA optimization manager comprises: a paging optimizerconfigured to: identify said at least one cell according to said pagingload exceeding an associated paging load threshold, and reduce saidpaging load for said at least one other cell by removing said at leastone other cell from said TA, wherein said at least one control signalload is paging load and said second control signal load is TAU load. 12.The TA optimization apparatus according to claim 1 wherein said TAoptimization manager comprises: a TAU optimizer configured to: identifysaid at least one cell according to said TAU load exceeding anassociated paging load threshold, and reduce said TAU load for said atleast one other cell by adding at least one additional cell from saidanother TA, wherein said at least one control signal load is TAU loadand said second control signal load is paging load.
 13. The TAoptimization apparatus according to claim 1 wherein said TA optimizationmanager further comprises: a TA shaper configured to: identify said atleast one cell according to at least one of said TAU load exceeding anassociated paging load threshold, or said paging load exceeding anassociated paging load threshold; use inter-cell handover successstatistics to identify a popular trajectory for user equipments (UEs)migrating through said at least one cell; and reconfigure said TA toform said reconfigured TA by removing at least one infrequently visitedcell from said TA, and adding frequently visited cells from said anotherTA, wherein said infrequently visited cells and said frequently visitedcells are identified according to said popular trajectory.
 14. The TAoptimization apparatus according to claim 13 wherein said TA shaper isfurther configured to: use TAU data to identify said popular trajectoryas a ping-pong trajectory, wherein said UEs cross back and forth overthe boundary of said TA and said another TA.
 15. The TA optimizationapparatus according to claim 13 wherein said TA optimization manager isfurther configured to: use TAU data to identify said at least one cellaccording to spikes in said TAU load, wherein said spikes are indicativeof multiple UEs expected to send TAUs in said TA in a short period oftime; and reduce said spikes by assigning at least two versions of saidTA for use by said multiple UEs when in said at least one cell, whereinsaid at least two versions have different lists of cells in said TA. 16.The TA optimization apparatus according to claim 1 and furthercomprising an input/output (I/O) module operative to: receive saidcontrol signal data and said updated control signal data from cells insaid mobile network; receive inter-cell handover success statistics fromsaid cells in said mobile network.
 17. The TA optimization apparatusaccording to claim 1 wherein said TA optimization apparatus isimplemented on a mobile management entity (MME).
 18. The TA optimizationapparatus according to claim 1 wherein said TA optimization manager isfurther configured to combine two or more TAs to form a single TA.
 19. Amethod for optimizing tracking area (TA) configurations in a mobilenetwork, the method implemented on a computing device and comprising:identifying at least one cell to optimize in a TA of a mobile network,wherein said at least one cell is associated with at least one signalload in excess of a defined signal load for said at least one cell,wherein said at least one signal load is at least one of a tracking areaupdate (TAU) load or a paging signal load; reconfiguring said TA to forma reconfigured TA by at least one of: adding at least one additionalcell from another TA to said TA, or removing at least one other cellfrom said TA; temporarily implementing said reconfigured TA in saidmobile network; for a configurable period of time, receiving updatedcontrol signal data associated with both said at least one controlsignal load and a second control signal load for cells affected by saidreconfigured TA; upon determining according to said updated controlsignal data that said at least one control signal load is under saidassociated control signal load threshold and that said second controlsignal load is under an associated second control signal load thresholdfor said cells affected by said reconfigured TA, save said reconfiguredTA for continued use in said mobile network.
 20. The method of claim 19,wherein said cells affected by said reconfigured TA include at least allother cells in said reconfigured TA.
 21. The method of claim 19, whereinsaid cells affected by said reconfigured TA include at least one cell inat least one neighboring TA, wherein said at least one neighboring TAwas reconfigured by said computing device performing at least one of:removing said one additional cell from said at least one neighboring TAto be added to said TA, or adding said at least one other cell removedfrom said TA to said at least one neighboring TA.
 22. The method ofclaim 19, further comprising using at least inter-cell handover successrate to determine which at least one additional cell from another TA toadd to said TA, and which at least one other cell to remove from saidTA.
 23. One or more non-transitory computer readable storage mediaencoded with instructions that, when executed by a processor, cause theprocessor to: identify at least one cell to optimize in a TA of a mobilenetwork, wherein said at least one cell is associated with at least onesignal load in excess of a defined signal load for said at least onecell, wherein said at least one signal load is at least one of atracking area update (TAU) load or a paging signal load; reconfiguresaid TA to form a reconfigured TA by at least one of: adding at leastone additional cell from another TA to said TA, or removing at least oneother cell from said TA; temporarily implement said reconfigured TA insaid mobile network; receive updated control signal data associated withboth said at least one control signal load and a second control signalload for cells affected by said reconfigured TA; save said reconfiguredTA for continued use in said mobile network upon determining accordingto said updated control signal data that said at least one controlsignal load is under said associated control signal load threshold andthat said second control signal load is under an associated secondcontrol signal load threshold for said cells affected by saidreconfigured TA.
 24. The one or more non-transitory computer readablestorage media of claim 23, wherein said cells affected by saidreconfigured TA include at least all other cells in said reconfiguredTA.
 25. The one or more non-transitory computer readable storage mediaof claim 23, wherein said cells affected by said reconfigured TA includeat least one cell in at least one neighboring TA, wherein said at leastone neighboring TA was reconfigured by said instructions being executedby the processor to perform at least one of: removing said oneadditional cell from said at least one neighboring TA to be added tosaid TA, or adding said at least one other cell removed from said TA tosaid at least one neighboring TA.
 26. The one or more non-transitorycomputer readable storage media of claim 23, wherein the instructions,when executed by the processor, further cause the processor to use atleast inter-cell handover success rate to determine which at least oneadditional cell from another TA to add to said TA, and which at leastone other cell to remove from said TA.